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Full Description
Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies .
Contents
Introduction 1 Part IChapter 1: Entering the Arena 7 Chapter 2: Predictive Analytics in the Wild 19 Chapter 3: Exploring Your Data Types and Associated Techniques 43 Chapter 4: Complexities of Data 57 Part II: Incorporating Algorithms in Your Models 73 Chapter 5: Applying Models 75 Chapter 6: Identifying Similarities in Data 89 Chapter 7: Predicting the Future Using Data Classification 115 Part III: Developing a Roadmap 145 Chapter 8: Convincing Your Management to Adopt Predictive Analytics 147 Chapter 9: Preparing Data 167 Chapter 10: Building a Predictive Model 177 Chapter 11: Visualization of Analytical Results 189 Part IV: Programming Predictive Analytics 205 Chapter 12: Creating Basic Prediction Examples 207 Chapter 13: Creating Basic Examples of Unsupervised Predictions 233 Chapter 14: Predictive Modeling with R 249 Chapter 15: Avoiding Analysis Traps 275 Chapter 16: Targeting Big Data 295 Part V: The Part of Tens 307 Chapter 17: Ten Reasons to Implement Predictive Analytics 309 Chapter 18: Ten Steps to Build a Predictive Analytic Model 319 Index 331
